Deep-SHAP analysis of a wall-sensing CNN for turbulent channel flow identifies wall pressure as the dominant input and high-importance regions as streak-like clusters of 20 to 120 wall units.
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Assessment of non-intrusive sensing in wall-bounded turbulence through explainable deep learning
Deep-SHAP analysis of a wall-sensing CNN for turbulent channel flow identifies wall pressure as the dominant input and high-importance regions as streak-like clusters of 20 to 120 wall units.